Advertisement

Multimedia Tools and Applications

, Volume 76, Issue 24, pp 26249–26271 | Cite as

Anomaly detection using sparse reconstruction in crowded scenes

  • Ang LiEmail author
  • Zhenjiang Miao
  • Yigang Cen
  • Yi Cen
Article

Abstract

In this paper, we propose an algorithm of anomaly detection in crowded scenes by using sparse representation over the normal bases. First, the histogram of maximal optical flow projection (HMOFP) features are extracted from a set of normal training data. Then, the online dictionary learning algorithm is used to train an optimal dictionary with proper redundancy, which is better than the dictionary simply composed by the HMOFP features of the whole training data. In order to detect the normalness of a frame, the l 1-norm of the sparse reconstruction coefficients is used as the Reconstruction Coefficient Sparsity (RCS). Our algorithm is effective for both global abnormal events (GAE) and local abnormal events (LAE). We evaluate our method on three benchmark datasets-the UMN dataset, the PETS2009 dataset and the UCSD Ped1 dataset. Compared with the most popular methods, experimental results show that our algorithm achieves good results especially for the pixel-level local abnormal event localization.

Keywords

HMOFP Online dictionary learning Sparse representation Abnormal events Crowded scenes 

Notes

Acknowledgments

This work is supported by the 973 Program (no. 2011CB302203), NSFC (nos. 61572067, 61272028, 61273274, 61672089, 61602538, and 61572064), PXM2016_014219_000025, National Key Technology R&D Program of China (no. 2012BAH01F03), NSFB (no. 4123104), Beijing Municipal Natural Science Foundation (no. 4162050), and Natural Science Foundation of Guangdong Province (no. 2016A030313708).

References

  1. 1.
    Aharon M, Elad M, Bruckstein A (2006) K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 54(11):4311–4322CrossRefzbMATHGoogle Scholar
  2. 2.
    Bi C, Wang H, Bao R (2014) SAR image change detection using regularized dictionary learning and fuzzy clustering [J]. IEEE Int Conf Cloud Comput Intell Syst (CCIS): 327–330Google Scholar
  3. 3.
    Cong Y, Yuan J, Liu J (2011) Sparse reconstruction cost for abnormal event detection. IEEE Conf Comput Vision Pattern Recogn (CVPR): 3449–3456Google Scholar
  4. 4.
    Donoho DL, Tsaic Y (2006) Extensions of compressed sensing. Signal Process 86(3):533–548CrossRefGoogle Scholar
  5. 5.
    Gu X, Cui J, Zhu Q (2014) Abnormal crowd behavior detection by using the particle entropy[J]. Optik-Int J Light Electron Optics 125(14):3428–3433CrossRefGoogle Scholar
  6. 6.
    Haque M, Murshed M (2010) Panic-driven event detection from surveillance video stream without track and motion features. IEEE Int Conf Multimed Expo (ICME): 173–178Google Scholar
  7. 7.
    Hung T, Lu J, Tan Y (2013) Cross-scene abnormal event detection. IEEE Int Sym Circ Syst (ISCAS): 2844–2847Google Scholar
  8. 8.
    Junior JSJ, Musse S, Jung C (2010) Crowd analysis using computer vision techniques. IEEE Signal Process Mag 5(27):66–77Google Scholar
  9. 9.
    Kosmopoulos D, Chatzis SP (2010) Robust visual behavior recognition. IEEE Signal Process Mag 27(5):34–45CrossRefGoogle Scholar
  10. 10.
    Lee CP, Lim KM, Woon WL (2010) Statistical and entropy based abnormal motion detection[C]. IEEE Student Conf Res Dev (SCOReD): 192–197Google Scholar
  11. 11.
    Li A, Miao Z, Cen Y, Wang T, Voronin V (2015) Histogram of maximal optical flow projection for abnormal events detection in crowded scenes. Int J Distrib Sensor Netw. doi: 10.1155/2015/406941 Google Scholar
  12. 12.
    Mahadevan V, Li W, Bhalodia V et al. (2010) Anomaly detection in crowded scenes. IEEE Conf Comput Vision Pattern Recogn (CVPR): 1975–1981Google Scholar
  13. 13.
    Mairal J, Bach F, Ponce J, Sapiro G (2009) Online dictionary learning for sparse coding. Proc 26th Ann Int Conf Mach Learn (ACM): 689–696Google Scholar
  14. 14.
    Mehran R, Oyama A, Shah M (2009) Abnormal crowd behavior detection using social force model. IEEE Conf Comput Vision Pattern Recogn (CVPR): 935–942Google Scholar
  15. 15.
    PETS (2009) Performance evaluation of tracking and surveillance (pets) 2009 benchmark data. http://www.cvg.reading.ac.uk/PETS2009/a.html
  16. 16.
    Quan Q, Hong-Yi C, Rui Z (2009) Entropy based method for network anomaly detection[C]. IEEE Pacific Rim Int Symp Depend Comput: 189–191Google Scholar
  17. 17.
    Ren H, Moeslund TB (2014) Abnormal event detection using local sparse representation. IEEE Int Conf Adv Video Sign Based Surveill (AVSS) 125–130Google Scholar
  18. 18.
    Rodriguez M, Ali S, Kanade T (2009) Tracking in unstructured crowded scenes. IEEE Int Conf Comput Vision: 1389–1396Google Scholar
  19. 19.
    Sandhan T, Srivastava T, Sethi A, Jin Y (2013) Unsupervised learning approach for abnormal event detection in surveillance video by revealing infrequent patterns. IEEE Int Conf Imag Vision Comput N Z (IVCNZ): 494–499Google Scholar
  20. 20.
    Shi Y, Gao Y, Wang R (2010) Real-time abnormal event detection in complicated scenes. IEEE Int Conf Pattern Recogn (ICPR): 3653–3656Google Scholar
  21. 21.
    Sjarif NNA, Shamsuddin SM, Hashim SZ (2011) Detection of abnormal behaviors in crowd scene: a review. Int J Adv Soft Comput Appl 3(3):1–33Google Scholar
  22. 22.
    Thida M, Yong YL, Climent-Pérez P, Eng H-l, Remagnino P (2013) A literature review on video analytics of crowded scenes. Intell Multimed Surveill: 17–36Google Scholar
  23. 23.
    Tropp J, Gilbert AC (2007) Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans Inf Theory 53(12):4655–4666MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Tziakos I, Cavallaro A, Xu L (2010) Local abnormal detection in video using subspace learning. IEEE Int Conf Adv Video Sign Based Surveill (AVSS): 519–525Google Scholar
  25. 25.
    UMN, Unusual crowd activity dataset of University of Minnesota, department of computer science and engineering. http://mha.cs.umn.edu/movies/crowd-activity-all.avi
  26. 26.
    Wang T, Snoussi H (2014) Detection of abnormal visual events via global optical flow orientation histogram. IEEE Trans Inform Forensics Sec 9(6):988–998CrossRefGoogle Scholar
  27. 27.
    Xie M, Hu J, Guo S (2015) Segment-based anomaly detection with approximated sample covariance matrix in wireless sensor network. IEEE Trans Parallel Distrib Syst 26(2):574–583CrossRefGoogle Scholar
  28. 28.
    Yang M, Dai D, Shen L et al. (2014) Latent dictionary learning for sparse representation based classification [C]. IEEE Comput Vision Pattern Recogn (CVPR): 4138–4145Google Scholar
  29. 29.
    Yang M, Zhang L, Feng X et al (2014) Sparse representation based fisher discrimination dictionary learning for image classification [J]. Int J Comput Vis 109(3):209–232MathSciNetCrossRefzbMATHGoogle Scholar
  30. 30.
    Yen S, Wang C (2013) Abnormal event detection using HOSF. Int Conf IT Converg Secur (ICITCS): 1–4Google Scholar
  31. 31.
    Zhan B et al (2008) Crowd analysis: a survey. Mach Vis Appl 19(5–6):345–357CrossRefGoogle Scholar
  32. 32.
    Zhang Y, Qin L, Yao H, Huang Q (2012) Abnormal crowd behavior detection based on social attribute-aware force model. IEEE Int Conf Imag Process (ICIP): 2689–2692Google Scholar
  33. 33.
    Zhang Y, Qin L, Yao H, Huang Q (2015) Social attribute-aware force model: exploiting richness of interaction for abnormal crowd detection. IEEE Trans Circ Syst Video Technol 25(7):1231–1245CrossRefGoogle Scholar
  34. 34.
    Zhu X, Liu J, Wang J, Li C, Lu H (2014) Sparse representation for robust abnormality detection in crowded scenes. Pattern Recogn 47(5):1791–1799CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Institute of Information ScienceBeijing Jiaotong UniversityBeijingChina
  2. 2.Beijing Key Laboratory of Advanced Information Science and Network TechnologyBeijingChina
  3. 3.School of Information EngineeringMinzu University of ChinaBeijingChina

Personalised recommendations